Friday, June 22, 2007

The Death of Significance?

by Tom Bozzo

At Decision Science News (another h/t to Brad DeLong), Dan Goldstein prints a comment from J. Scott Armstrong who has "concluded that tests of statistical significance should never be used." [Emphasis mine.] He is not conducting statistical performance art, and I substantially agree with the conclusion. A couple random remarks:
Armstrong's reasonable recommendations are:
Authors... instead... should report on effect sizes, confidence intervals, replications/extensions, and meta-analyses.
For those of you with institutional access, links to the International Journal of Forecasting article are at the Decision Science News link.

(Cross-posted at Total Drek.)

(*) This sometimes leads to wacky advice being given to everyday applied researchers from econo- or sociometricians, of the "if a result from an inconsistent esitmator goes away with a consistent (but inefficient) procedure, be suspicious [or vice-versa]." Armstrong's bottom-line recommendations address the reasonable suspicions that might arise.

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Apologies if this is redundant:

In particular:

"the difference between "significant" and "not significant" is not itself statistically significant." at
I have a horrible feeling, judging strictly by that "in particular," that statistics has just discovered Convexity.

Can acceleration be far behind?
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